### University of Kentucky

# MA 721: Topics in Numerical Analysis: Deep Learning

### Fall 2017

### MWF 1:00 pm - 1:50 pm, CB 347

## Instructor

Dr. Qiang Ye

Office: 735 Patterson Office Tower

Phone:257-4653

Email: qye3 "at" uky . edu

Office Hours: MWF 2:00-3:00 pm

## Textbook

There will be no required text, but the following books will be good references:
## Syllabus

In this course, we study a widely applicable class of machine learning methods called deep learning. We will cover the following topics:
- Deep Feedforward Networks.
- Convolutional Neural Networks
- Recurrent Neural Networks
- Deep Generative Models for Unsupervised Learning

Selected materials from optimization, linear algebra, and probability theory/information theory will be covered.
## Prerequisites

Familiarity with multivariate calculus, linear algebra and numerical methods will be assumed. Programming in Python will be required.
## Grading

The course grade will be based on programming projects (60%) and an in-class presentation (40%).
## Course Materials

## Computing

We will use Python based toolboxs such as Keras, Theano, or Tensorflow. You may choose to use any of them. It is best to run them on the Linux platform. But if you use Windows, below are some links for setting up Keras/Theano/Tensorflow on Windows 10.
You may also install these toolboxes using
ANACONDA DISTRIBUTION. Download the distribution and try the following (Thanks to Liu Liu for these tips!)
- For windows, just double click and install. Choose 'add anaconda to PATH environment', and 'use anaconda as default python' options if you see them.
After installation, open anaconda navigator to install all the packages you need (theano, tensorflow, keras) in 'Environments'.
- For linux, run 'bash ~/Downloads/Anacondaxxx.sh' in terminal to install the downloaded anaconda. Choose 'add to PATH environment', and 'use anaconda as default python' options if you see them.
After installation, run 'anaconda-navigator' in terminal to open anaconda navigator to install all the packages you need (theano, tensorflow, keras) in 'Environments'.

## Some references and links

Below are some links and books on numerical linear algebra, optimization, and machine learning that may be helpful.